UrbanShift is a global program that supports cities to adopt integrated approaches to urban development, shaping low-carbon, climate-resilient communities where people and planet both can thrive. The global program is funded by the Global Environment Facility (GEF) led by the UN Environment Programme (UNEP), in partnership with the World Resources Institute (WRI), C40 Cities, ICLEI – Local Governments for Sustainability. The initiative supports 23 cities across nine countries, providing the knowledge, tools and training they need to transform their urban fabric and shift towards a more sustainable, equitable future.
As one of the key activities to support UrbanShift cities , the WRI data team will work with UrbanShift cities to identify and provide all cities with a common set of critical spatial data layers. using open source, global data. World Resources Institute is providing several types of data-related assistance to participating cities:
Outputs will include datasets, indicators and replicable analysis methods relevant to all cities. Additionally, analyses customized to the specific themes of interest for each city will be provided. Finally, an UrbanShift Lab will be delivered for which these data and analyses may act as one input.
To help understand the current status and identify changes of sustainability in UrbanShift cities, we aim to measure key baseline indicators for all cities using comparable approaches. The selected indicators focus on measuring the status and change of the core objectives of the global project, which are aligned with three of Global Environment Facility’s focal areas for its current investment cycle (GEF-7):land degradation, biodiversity, and greenhouse gas emissions.
These assessments are intended to support the evaluation of patterns within and between cities and to provide contextual information to cities to help them in the deision making process. We will disseminate the results to help local governments, the global project team, implementing agencies and national governments to gain a better understanding of the cities’ current status as it relates to sustainability efforts, capacities, needs and opportunities, and planned investments.
Land degradation is one of the world’s most pressing environmental challenge with direct impacts on climate change adaptation, ecosystem condition, food security and human well-being. Globally, about 25% of the total land area has been degraded and 3.2 billion people are affected by this phenomenon, particularly rural communities, smallholder farmers, and the very poor (source). As a financial mechanism of the United Nations Convention to Combat Desertification (UNCDD), the GEF is highlighting the necessity to invest in programs that encourage sustainable land management practices and land degradation has been selected as one of the strategic focal area in its new four-year investment cycle (known as GEF-7).
Through the Land Degradation Neutrality (LDN) program, the UNCDD, in collaboration with multiple international partners, is supporting interested countries in setting national baselines, targets and measures to protect their land resources. The Land Degradation Neutrality concept is defined as a state whereby the amount and quality of land resources necessary to support ecosystem function and services and enhance food security remain stable or increase within specified temporal and spatial scales and ecosystems.
These objectives are in accordance with the Sustainable Development Goal (SDG) target 15.3 stating: By 2030, combat desertification, restore degraded land and soil, including land affected by desertification, drought and floods, and strive to achieve a land degradation neutral world.
The Special Report on Climate Change and Land, defines Land degradation as negative trend in land condition, caused by direct or indirect human-induced processes including anthropogenic climate change, expressed as long-term reduction or loss of at least one of the following: biological productivity, ecological integrity, or value to humans.
Multiple factors are increasing the pressure on land resources such as the growing demand for food, urban expansion, decrease in land productivity due to soil degradation, biodiversity loss and extreme weather events.
In order to provide greenspace indicators within UrbanShift cities, we selected global coverage datasets with high spatial resolution.
Dynamic world (DW): The Dynamic World Land Cover product displays a global map of land use/land cover (LULC) provided from ESA Sentinel-2 imagery at 10m resolution. It is composed of 10 land use classes: water, trees,grass,flooded vegetation,crops,scrub/shrub, built area,bare ground, and snow/ice. The DW datasets can be used as a proxy for estimating land degradation by quantifying the percent of vegetation land area (such as water, trees,grass) within UrbanShift cities boundaries.
Sentinel-2: Sentinel-2 is a wide-swath, high-resolution, multi-spectral imaging mission supporting Copernicus Land Monitoring studies, including the monitoring of vegetation, soil and water cover. Its optical instrument samples in 13 spectral bands: four bands at 10 meters, six bands at 20 meters and three bands at 60 meters spatial resolution. Sentinel-2 images can be used for computing the Normalized Difference Vegetation Index (NDVI) considered as an effective index for estimating green vegetation.
Tree Outside of Forests (TOF): The TOF project provides tree extent data at 10m scale based on trained Convolutional Neural Network using satellite imagery (Sentinel-1 and Sentinel-2). It enables accurate reporting of tree cover outside of dense, closed-canopy forests and urban areas. For more details about the data, see the github repository and this article. The TOF data is used for estimating tree cover within the selected UrbanShift cities.
Intra-Urban Land Use (ULU): The ULU data provides land use and land cover information of urban areas based on the application of supervised classification model trained on high resolution Sentinel-2 satellite imagery data. Urban land classes include: open space,non residential area,residential atomistic,residential informal,residential forma,housing project, and `roads``. (detailed documentation of this dataset. This dataset provides the distribution of different urban land use classes within UrbanShift cities’ boundaries and statistics on vegetation and tree cover levels by land use classes
Based on the previously identified datasets, we propose to compute a list of indicators that enable us to assess land degradation and greenspace status within UrbanShift cities. The table below lists the different indicators we are measuring in this analysis:
| Indicator name | Description | Used datasets | Years |
|---|---|---|---|
| Percent of vegetation land based on Dynamic World land cover classes | Percent of land that is trees/water/grass/Scrub/flooded vegetation land cover | Dynamic World Land cover | [2016,2020] |
| Percent of vegetation land based on NDVI threshold | Percent of land that is vegetation (NDVI threshold > 0.4) | Sentinel-2 | [2016,2020] |
| Percent of land with tree cover | Percent of land that has tree cover | Tree Outside of Forests (TOF) | [2020] |
| Percent of built area with tree cover | Percent of land that has tree cover | Dynamic World Land cover, Tree Outside of Forests (TOF) | [2020] |
| Percent of built area with vegetation | Percent of built area with vegetation based on NDVI threshold | Dynamic World Land cover, Sentinel-2 | [2016,2020] |
| Percent of Intra-Urban land classes | Percent of land based on Urban Land Use classification: Open space,Residential,Atomistic,Informal subdivision, Formal subdivision,Housing projects. |
Intra-Urban Land Use | [2020] |
| Percent of tree cover by urban land classes | Percent of tree cover level (as expressed in Tree Outside of Forests dataset) by intra-urbal land use classes | Intra-Urban Land Use, Tree Outside of Forests (TOF) | [2020] |
| Percent of vegetation by urban land classes | Percent of vegetation (based on NDVI threshold) by intra-urbal land use classes | Intra-Urban Land Use, Sentinel-2 | [2020] |
The administrative boundaries data is obtained from the geoBoundaries database. Produced and maintained by the William & Mary geoLab and open data community since 2017, the geoBoundaries Global Database of Political Administrative Boundaries Database is an online, open license resource of boundaries (i.e., state, county) for every country in the world.
The administrative boundaries are used for extracting and aggregating geospatial information and indicators based on the city extent.
This Dynamic World Land Cover product displays a global map of land use/land cover (LULC) provided from ESA Sentinel-2 imagery at 10m resolution. It is composed of 10 land use classes based on a deep learning model. The class definition is as follows (Reference):
The map below displays the spatial distribution of Land cover for the selected city on 2020:
| city_id | indicator_theme | data_sources | indicator_name | year | value |
|---|---|---|---|---|---|
| RWA-Kigali | greenspace | Dynamic WOrld | dynamic_world_vegetation_land_percent | 2016 | 49.22 |
| RWA-Kigali | greenspace | Dynamic WOrld | dynamic_world_vegetation_land_percent | 2017 | 44.15 |
| RWA-Kigali | greenspace | Dynamic WOrld | dynamic_world_vegetation_land_percent | 2018 | 34.90 |
| RWA-Kigali | greenspace | Dynamic WOrld | dynamic_world_vegetation_land_percent | 2019 | 39.11 |
| RWA-Kigali | greenspace | Dynamic WOrld | dynamic_world_vegetation_land_percent | 2020 | 40.17 |
Sentinel-2 is a wide-swath, high-resolution, multi-spectral imaging mission supporting Copernicus Land Monitoring studies, including the monitoring of vegetation, soil and water cover, as well as observation of inland waterways and coastal areas. The Sentinel-2 data contain 13 UINT16 spectral bands: four bands at 10 m, six bands at 20 m and three bands at 60 m spatial resolution.
Sentinel-2 images can be used for computing the Normalized Difference Vegetation Index (NDVI) considered as an effective index for estimating green vegetation. The value range of the NDVI is -1 to 1. Negative values of NDVI (values approaching -1) correspond to water. Values close to zero (-0.1 to 0.1) generally correspond to barren areas of rock, sand, or snow. Low, positive values represent shrub and grassland (approximately 0.2 to 0.4), while high values indicate temperate and tropical rainforests (values approaching 1). It is a good proxy for live green vegetation. A threshold of NDVI > 0.4 is used in this analysis for identifying vegetation area based on Sentinel-2 images.
\[NDVI = \frac{NIR(Band 8) - RED(Band 4)}{NIR(Band 8)+ RED(Band 4))}\]
| city_id | indicator_theme | data_sources | indicator_name | year | value |
|---|---|---|---|---|---|
| RWA-Kigali | greenspace | COPERNICUS/S2 | s2_ndvi_vegetation_land_percent | 2020 | 87.29 |
| RWA-Kigali | greenspace | COPERNICUS/S2 | s2_ndvi_vegetation_land_percent | 2019 | 88.61 |
| RWA-Kigali | greenspace | COPERNICUS/S2 | s2_ndvi_vegetation_land_percent | 2018 | 90.06 |
| RWA-Kigali | greenspace | COPERNICUS/S2 | s2_ndvi_vegetation_land_percent | 2017 | 86.33 |
| RWA-Kigali | greenspace | COPERNICUS/S2 | s2_ndvi_vegetation_land_percent | 2016 | 88.20 |
The Tree Outside of Forests data enables the reporting of tree cover within urban areas. Only data corresponding to the year 2020 is available. The proposed indicator measures the average tree cover percent within the selected city.
| city_id | indicator_theme | data_sources | indicator_name | year | value |
|---|---|---|---|---|---|
| RWA-Kigali | greenspace | Tree Outside Forests | tof_avg_tree_cover | 2020 | 8.59 |
This indicator measures the percentage of vegetation in built areas by combining the Dynamic World land classes to extract built areas and Sentinel-2 imagery to estimate vegetation index based on the NDVI metric.
| city_id | indicator_theme | data_sources | indicator_name | year | value |
|---|---|---|---|---|---|
| RWA-Kigali | greenspace | Dynamic World / Sentinel-2 | built_land_cover_with_vegetation_percent | 2020 | 38.87 |
| RWA-Kigali | greenspace | Dynamic World / Sentinel-2 | built_land_cover_with_vegetation_percent | 2019 | 40.81 |
| RWA-Kigali | greenspace | Dynamic World / Sentinel-2 | built_land_cover_with_vegetation_percent | 2018 | 40.73 |
| RWA-Kigali | greenspace | Dynamic World / Sentinel-2 | built_land_cover_with_vegetation_percent | 2017 | 37.06 |
| RWA-Kigali | greenspace | Dynamic World / Sentinel-2 | built_land_cover_with_vegetation_percent | 2016 | 34.17 |
This indicator measures the tree cover of built areas by combining the Dynamic World land classesto extract built areas and TOF data for tree cover levels.
| city_id | indicator_theme | data_sources | indicator_name | year | value |
|---|---|---|---|---|---|
| RWA-Kigali | greenspace | Dynamic World / Tree Outside Forests | built_land_with_tree_cover_percent | 2020 | 6.947661 |
The ULU data provides land use and land cover information of urban areas based on the application of a supervised classification model trained on high resolution Sentinel-2 satellite imagery data. Urban land classes include:
This indicator measures the percent of tree cover (based on TOF dataset) and vegetation (based on Sentinel2 imagery) by urban land use classes.
| Urban Land Use Class code | Urban Land Use Class label | Tree cover percent | Year | City id | Vegetation percent |
|---|---|---|---|---|---|
| 0 | open_space | 8.98 | 2020 | RWA-Kigali | 94.97 |
| 1 | nonresidential | 9.33 | 2020 | RWA-Kigali | 27.57 |
| 2 | atomistic | 2.70 | 2020 | RWA-Kigali | 17.17 |
| 3 | informal_subdivision | 4.89 | 2020 | RWA-Kigali | 29.11 |
| 4 | formal_subdivision | 14.75 | 2020 | RWA-Kigali | 37.63 |
| 5 | housing_project | 6.56 | 2020 | RWA-Kigali | 32.75 |